Materials Dual-Source Knowledge Retrieval-Augmented Generation for Local Large Language Models in Photocatalysts.

ABSTRACT: Large language models (LLMs) have the potential to serve as collaborative assistants in scientific research. However, adapting them to specialized domains is difficult because it requires the integration of domain-specific knowledge. We propose Materials Dual-Source Knowledge Retrieval-Augmented Generation (MDSK-RAG), a retrieval-augmented generation (RAG) framework that enables domain specialization of LLMs for materials development under fully offline (no-Internet) operation to ensure data confidentiality. The framework unifies two complementary knowledge sources, experimental CSV data (practical knowledge) and scientific PDF literature (theoretical insights), by converting tabular records into template-based text, retrieving relevant passages from each source, summarizing them with a lo

SUBMITTER: Takahara W 

PROVIDER: S-EPMC12728951 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature